Abstract
Preserving facial identity under extreme stylistic transformation remains a major challenge in generative art. In graffiti, a high-contrast, abstract medium, subtle distortions to the eyes, nose, or mouth can erase the subject's recognizability, undermining both personal and cultural authenticity. We present CraftGraffiti, an end-to-end text-guided graffiti generation framework designed with facial feature preservation as a primary objective. Given an input image and a style and pose descriptive prompt, CraftGraffiti first applies graffiti style transfer via LoRA-fine-tuned pretrained diffusion transformer, then enforces identity fidelity through a face-consistent self-attention mechanism that augments attention layers with explicit identity embeddings. Pose customization is achieved without keypoints, using CLIP-guided prompt extension to enable dynamic re-posing while retaining facial coherence. We formally justify and empirically validate the "style-first, identity-after" paradigm, showing it reduces attribute drift compared to the reverse order. Quantitative results demonstrate competitive facial feature consistency and state-of-the-art aesthetic and human preference scores, while qualitative analyses and a live deployment at the Cruilla Festival highlight the system's real-world creative impact. CraftGraffiti advances the goal of identity-respectful AI-assisted artistry, offering a principled approach for blending stylistic freedom with recognizability in creative AI applications.
Abstract (translated)
在极端风格变换中保持面部身份识别是一个生成艺术中的重大挑战。在涂鸦这种高对比度、抽象的艺术形式中，对眼睛、鼻子或嘴巴的细微扭曲可能会抹去主体的可辨识性，从而削弱个人和文化的独特性。我们推出了CraftGraffiti，这是一个端到端的文本引导式涂鸦生成框架，旨在以面部特征保真为主要目标。 给定输入图像以及描述风格与姿态的文字提示后，CraftGraffiti首先通过使用LoRA微调预训练扩散变换器来应用涂鸦风格转移。然后，它通过一个增强注意力层并添加显式身份嵌入的面一致自我注意机制来确保身份保真度。姿势定制在无需关键点的情况下完成，通过CLIP引导的文字提示扩展实现动态重新定位的同时保持面部一致性。 我们正式证明并在实证上验证了“风格优先、身份随后”的范例，表明与相反顺序相比，这种方法可以减少属性漂移（attribute drift）。定量结果显示了具有竞争力的面部特征一致性和美学以及人类偏好的最佳评分。定性分析和在Cruilla音乐节上的实际部署突显了该系统的现实世界创意影响力。 CraftGraffiti推进了尊重身份的AI辅助艺术的目标，提供了一种将风格自由度与可辨识性相结合的原则方法，在创造性的AI应用中开辟新的领域。
URL
https://arxiv.org/abs/2508.20640
